R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4264830
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+ ,1.7178
+ ,2.9923
+ ,.4809
+ ,1.0840
+ ,1.2478
+ ,1.9650
+ ,8161117
+ ,890943.7000
+ ,2072759
+ ,5055640
+ ,10599314
+ ,3798478
+ ,1.2506
+ ,2.4275
+ ,2.2874
+ ,2.6884
+ ,2.6527
+ ,3.5067
+ ,1.8806
+ ,1.6938
+ ,2.9913
+ ,.4790
+ ,1.0473
+ ,1.2443
+ ,1.9478
+ ,8085780
+ ,818812.4000
+ ,2201360
+ ,5234681
+ ,10501150
+ ,3644313
+ ,1.2638
+ ,2.4233
+ ,2.2936
+ ,2.7027
+ ,2.6711
+ ,3.5260
+ ,1.9179
+ ,1.7262
+ ,2.9611
+ ,.4959
+ ,1.0635
+ ,1.2874
+ ,1.9661
+ ,7777563
+ ,813389.4000
+ ,2215184
+ ,5456357
+ ,9476948
+ ,3784029
+ ,1.2661
+ ,2.3870
+ ,2.3180
+ ,2.7110
+ ,2.6323
+ ,3.5443
+ ,1.9145
+ ,1.7150
+ ,2.9894
+ ,.4800
+ ,1.0403
+ ,1.2843
+ ,1.9592
+ ,8192525
+ ,791213.0000
+ ,2140796
+ ,5055154
+ ,9854999
+ ,3647134
+ ,1.2596
+ ,2.3949
+ ,2.3151
+ ,2.6879
+ ,2.6333
+ ,3.5167
+ ,1.9304
+ ,1.7398
+ ,2.9760
+ ,.4827
+ ,1.0607
+ ,1.2593
+ ,1.9530
+ ,8222640
+ ,753161.9000
+ ,2064345
+ ,4986559
+ ,9020688
+ ,3994662
+ ,1.2542
+ ,2.3853
+ ,2.3403
+ ,2.7191
+ ,2.6491
+ ,3.4653
+ ,1.9046
+ ,1.7543
+ ,3.0096
+ ,.4829
+ ,1.0644
+ ,1.2245
+ ,1.9561
+ ,8852425
+ ,744738.3000
+ ,2246763
+ ,5314687
+ ,9639666
+ ,3607836
+ ,1.2539
+ ,2.4133
+ ,2.3204
+ ,2.7146
+ ,2.5823
+ ,3.4967
+ ,1.9119
+ ,1.7148
+ ,2.9680
+ ,.4740
+ ,1.0712
+ ,1.2311
+ ,1.9576
+ ,8047626
+ ,740853.2000
+ ,2196948
+ ,5029952
+ ,10016963
+ ,3566008
+ ,1.2548
+ ,2.3681
+ ,2.1607
+ ,2.7095
+ ,2.3672
+ ,3.5128
+ ,1.8923
+ ,1.7246
+ ,2.9793
+ ,.4772
+ ,1.0848
+ ,1.2505
+ ,1.9549
+ ,8079925
+ ,828505.4000
+ ,1987852
+ ,4569712
+ ,9221363
+ ,3511412
+ ,1.2606
+ ,2.3832
+ ,1.8827
+ ,2.7356
+ ,2.6429
+ ,3.5276
+ ,1.9149
+ ,1.7249
+ ,2.9455
+ ,.4789
+ ,1.0773
+ ,1.2476
+ ,1.9183
+ ,8099820
+ ,764325.4000
+ ,2013311
+ ,4661941
+ ,9163961
+ ,3258665
+ ,1.2614
+ ,2.4082
+ ,2.0185
+ ,2.8202
+ ,2.6340
+ ,3.5227
+ ,1.9172
+ ,1.7247
+ ,2.9485
+ ,.4806
+ ,1.0542
+ ,1.2487
+ ,1.9012
+ ,7444464
+ ,779151.6000
+ ,2024477
+ ,4649692
+ ,9600997
+ ,3486573
+ ,1.2626
+ ,2.4641
+ ,2.0727
+ ,2.7569
+ ,2.6312
+ ,3.5438
+ ,1.9138
+ ,1.7178
+ ,2.9581
+ ,.4816
+ ,1.0385
+ ,1.2677
+ ,1.9330
+ ,8060967
+ ,780635.1000
+ ,2175719
+ ,4883549
+ ,9629093
+ ,3369443
+ ,1.2719
+ ,2.4734
+ ,2.2192
+ ,2.7722
+ ,2.7039
+ ,3.5446
+ ,1.9508
+ ,1.7363
+ ,3.0063
+ ,.4832
+ ,1.0442
+ ,1.2609
+ ,1.9171
+ ,7904184
+ ,772651.9000
+ ,2459717
+ ,4927239
+ ,9266651
+ ,3465544
+ ,1.2798
+ ,2.4978
+ ,2.3992
+ ,2.7747
+ ,2.6937
+ ,3.5515
+ ,1.9510
+ ,1.7367
+ ,3.0513
+ ,.4853
+ ,1.0433
+ ,1.2563
+ ,1.9332
+ ,8532755
+ ,796750.8000
+ ,2436148
+ ,5077345
+ ,11454028
+ ,3905224
+ ,1.2909
+ ,2.5224
+ ,2.4687
+ ,2.8096
+ ,2.6996
+ ,3.5635
+ ,1.9719
+ ,1.7840
+ ,3.0844
+ ,.4955
+ ,1.0402
+ ,1.2499
+ ,1.8988
+ ,10077590
+ ,774563.7000
+ ,2533141
+ ,4551562
+ ,10051577
+ ,3733881
+ ,1.3027
+ ,2.5336
+ ,2.4277
+ ,2.7732
+ ,2.6797
+ ,3.5866
+ ,1.9727
+ ,1.7663
+ ,3.0785
+ ,.4900
+ ,1.0589
+ ,1.2522
+ ,1.9184
+ ,9163186
+ ,781544.8000
+ ,2438635
+ ,4807379
+ ,8887058
+ ,3220642
+ ,1.2565
+ ,2.5136
+ ,2.3863
+ ,2.7560
+ ,2.6850
+ ,3.5616
+ ,1.9428
+ ,1.7169
+ ,3.0362
+ ,.4892
+ ,1.0823
+ ,1.2450
+ ,1.9011
+ ,7027349
+ ,846743.7000
+ ,2294455
+ ,5560041
+ ,9590767
+ ,3225812
+ ,1.2509
+ ,2.4282
+ ,2.3860
+ ,2.7331
+ ,2.6891
+ ,3.5248
+ ,1.9311
+ ,1.7318
+ ,3.0287
+ ,.5220
+ ,1.0825
+ ,1.2487
+ ,1.8955
+ ,7000371
+ ,852582.7000
+ ,2233829
+ ,5637553
+ ,9269821
+ ,3354461
+ ,1.2624
+ ,2.5319
+ ,2.3886
+ ,2.7156
+ ,2.6870
+ ,3.5410
+ ,1.9221
+ ,1.7135
+ ,3.0139
+ ,.5214
+ ,1.0805
+ ,1.2335
+ ,1.8871
+ ,7234027
+ ,837685.9000
+ ,2231864
+ ,5502635
+ ,9242497
+ ,3352261
+ ,1.2486
+ ,2.5218
+ ,2.3603
+ ,2.7327
+ ,2.6654
+ ,3.5510
+ ,1.9338
+ ,1.7101
+ ,3.0868
+ ,.5145
+ ,1.0685
+ ,1.2267
+ ,1.8848
+ ,7166769
+ ,872753.1000
+ ,2248620
+ ,5354221
+ ,9621983
+ ,3450652
+ ,1.2383
+ ,2.4867
+ ,2.3185
+ ,2.7075
+ ,2.6610
+ ,3.5552
+ ,1.9111
+ ,1.7224
+ ,3.1237
+ ,.5383
+ ,1.0613
+ ,1.2389
+ ,1.8759
+ ,7538708
+ ,863745.7000
+ ,2348107
+ ,5707447
+ ,10101244)
+ ,dim=c(19
+ ,130)
+ ,dimnames=list(c('QBEPIL'
+ ,'PBEPIL'
+ ,'PBELUX'
+ ,'PBABD'
+ ,'PBFRU'
+ ,'PBEPAL'
+ ,'PBESTO'
+ ,'PBEWIT'
+ ,'PBENA'
+ ,'PCHSAN'
+ ,'PWABR'
+ ,'PSOCOLA'
+ ,'PSOBIT'
+ ,'PSPORT'
+ ,'BUDBEER'
+ ,'BUDCHIL'
+ ,'BUDAMB'
+ ,'BUDWATER'
+ ,'BUDSISSS')
+ ,1:130))
> y <- array(NA,dim=c(19,130),dimnames=list(c('QBEPIL','PBEPIL','PBELUX','PBABD','PBFRU','PBEPAL','PBESTO','PBEWIT','PBENA','PCHSAN','PWABR','PSOCOLA','PSOBIT','PSPORT','BUDBEER','BUDCHIL','BUDAMB','BUDWATER','BUDSISSS'),1:130))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
QBEPIL PBEPIL PBELUX PBABD PBFRU PBEPAL PBESTO PBEWIT PBENA PCHSAN
1 4264830 1.2299 2.4507 2.3512 2.5048 2.6602 3.2726 1.7959 1.8637 2.6811
2 3924674 1.2209 2.4601 2.3169 2.5889 2.6139 3.2211 1.7877 1.8556 2.6060
3 3734753 1.2130 2.4453 2.3552 2.5567 2.6274 3.2333 1.7796 1.8391 2.5823
4 3762290 1.2207 2.4276 2.2755 2.5940 2.6533 3.2340 1.7974 1.8207 2.5901
5 3609739 1.2137 2.4355 2.2578 2.5750 2.6374 3.2505 1.7887 1.8006 2.5352
6 3877594 1.2196 2.4066 2.3053 2.6151 2.6382 3.2437 1.7605 1.8392 2.5723
7 3636415 1.2114 2.4084 2.2789 2.5942 2.6430 3.2608 1.7584 1.8342 2.6363
8 3578195 1.2032 2.4098 2.1956 2.5757 2.5574 3.2483 1.7660 1.7863 2.6663
9 3604342 1.1824 2.4088 2.2423 2.5748 2.5670 3.2275 1.7650 1.8080 2.6378
10 3459513 1.1912 2.3769 2.3296 2.5705 2.6246 3.2059 1.7373 1.7833 2.6311
11 3366571 1.1973 2.4100 2.3557 2.5451 2.4956 3.2362 1.7508 1.7409 2.6072
12 3371277 1.1887 2.3860 2.2274 2.5129 2.4101 3.2215 1.6954 1.7380 2.6166
13 3724848 1.1851 2.3731 2.3054 2.4987 2.5017 3.2090 1.7146 1.7689 2.6291
14 3350830 1.1963 2.4014 2.2742 2.5856 2.4824 3.2480 1.7681 1.7627 2.4861
15 3305159 1.2080 2.3548 2.2849 2.6253 2.5210 3.2653 1.7747 1.7404 2.5230
16 3390736 1.2037 2.3503 2.3097 2.6340 2.5352 3.2335 1.7722 1.7671 2.5187
17 3349758 1.1998 2.3330 2.4261 2.6101 2.5073 3.2509 1.7721 1.7462 2.5590
18 3253655 1.2025 2.3530 2.4569 2.6377 2.5171 3.2511 1.7673 1.7820 2.6114
19 3734250 1.2096 2.3584 2.4756 2.6708 2.4904 3.2301 1.7677 1.7846 2.6088
20 3455433 1.2129 2.4147 2.4517 2.6289 2.4905 3.2194 1.7942 1.7887 2.5935
21 2966726 1.2098 2.3653 2.4406 2.5792 2.5133 3.2309 1.7928 1.7778 2.4121
22 2993716 1.1991 2.3449 2.3625 2.5571 2.4577 3.2406 1.7610 1.7439 2.2854
23 3009320 1.2067 2.3720 2.3382 2.5742 2.4979 3.2489 1.7550 1.7631 2.5536
24 3169713 1.2141 2.3396 2.1605 2.5495 2.4990 3.2397 1.7832 1.7628 2.5642
25 3170061 1.2075 2.3357 2.2267 2.5774 2.5186 3.2401 1.8055 1.7743 2.7059
26 3368934 1.1996 2.3283 2.1558 2.4984 2.5816 3.2904 1.7660 1.8370 2.6862
27 3292638 1.1920 2.3327 2.2009 2.5635 2.5377 3.2526 1.7456 1.8516 2.7393
28 3337344 1.1987 2.3373 2.2594 2.6249 2.5318 3.2815 1.7672 1.8406 2.7516
29 3208306 1.2005 2.3722 2.2282 2.7095 2.5655 3.3185 1.7230 1.8294 2.8767
30 3359130 1.1991 2.3756 2.3156 2.7447 2.5948 3.2888 1.7993 1.8273 2.9164
31 3223078 1.2161 2.4096 2.3361 2.7552 2.5921 3.3127 1.7701 1.8204 2.9822
32 3437159 1.2192 2.3883 2.3333 2.6562 2.5815 3.3319 1.7959 1.8379 2.9503
33 3400156 1.2104 2.3770 2.3043 2.6111 2.5861 3.3254 1.7927 1.8380 2.9161
34 3657576 1.2529 2.4496 2.3170 2.6759 2.5917 3.3463 1.8160 1.6534 2.9694
35 3765613 1.2532 2.4084 2.3103 2.6966 2.5327 3.3515 1.8063 1.6417 2.9796
36 3481921 1.2705 2.4551 2.3220 2.6982 2.5768 3.3587 1.8008 1.6593 2.9542
37 3604800 1.2802 2.3990 2.3220 2.7157 2.5420 3.3755 1.7556 1.6523 2.9450
38 3981340 1.2748 2.3142 2.2987 2.7660 2.5773 3.3235 1.7315 1.6354 2.8637
39 3734078 1.2799 2.4180 2.3263 2.7644 2.6154 3.3498 1.7731 1.6506 2.9262
40 4018173 1.2903 2.4613 2.3407 2.7209 2.6333 3.3459 1.7818 1.6491 2.9362
41 3887417 1.2631 2.4524 2.3224 2.6943 2.6373 3.3431 1.7717 1.6786 2.9432
42 3919880 1.2666 2.4768 2.3247 2.6983 2.6347 3.3052 1.7450 1.6621 2.9038
43 4014466 1.2546 2.4489 2.3244 2.6916 2.6286 3.3063 1.7503 1.6614 2.9245
44 4197758 1.2654 2.4547 2.3308 2.6625 2.6616 3.3537 1.7840 1.6638 2.8455
45 3896531 1.2664 2.4281 2.3628 2.7398 2.5881 3.3691 1.7585 1.6321 2.8777
46 3964742 1.2746 2.4398 2.3711 2.7613 2.6002 3.3620 1.7305 1.6436 2.8599
47 4201847 1.2869 2.4573 2.3455 2.7940 2.6299 3.3723 1.7690 1.6567 2.8622
48 4050512 1.2640 2.4781 2.2756 2.7806 2.6236 3.3797 1.7805 1.6795 2.8419
49 3997402 1.2687 2.5170 2.2058 2.8034 2.6486 3.3841 1.8024 1.6771 2.8561
50 4314479 1.2695 2.5142 2.2569 2.7773 2.6384 3.3972 1.8131 1.6942 2.8896
51 4925744 1.2798 2.5001 2.3198 2.7553 2.6399 3.3655 1.7896 1.7070 2.9099
52 5130631 1.2790 2.4973 2.3273 2.7306 2.6294 3.3907 1.7880 1.7134 2.9106
53 4444855 1.2752 2.5323 2.3559 2.7223 2.6462 3.4323 1.7946 1.6913 2.8710
54 3967319 1.2689 2.5427 2.3555 2.7320 2.6138 3.3459 1.7622 1.6844 2.8542
55 3931250 1.2431 2.5384 2.3526 2.7351 2.6020 3.3846 1.7782 1.6642 2.9097
56 4235952 1.2487 2.5344 2.3427 2.7229 2.6324 3.4067 1.8108 1.6549 2.9152
57 4169219 1.2476 2.4775 2.3204 2.7095 2.6391 3.3927 1.8177 1.6651 2.9210
58 3779064 1.2441 2.4689 2.2714 2.6582 2.6173 3.3851 1.7963 1.6442 2.9124
59 3558810 1.2378 2.4438 2.3035 2.6773 2.6178 3.3995 1.7779 1.6246 2.8250
60 3699466 1.2340 2.4396 2.2903 2.6688 2.6119 3.3647 1.7589 1.6181 2.8404
61 3650693 1.2351 2.4303 2.3019 2.6843 2.5892 3.3928 1.7638 1.6444 2.8615
62 3525633 1.2339 2.4120 2.3036 2.6748 2.4352 3.3794 1.7634 1.5997 2.6592
63 3470276 1.2389 2.4174 2.3582 2.7083 2.4701 3.3686 1.7728 1.6014 2.7440
64 3859094 1.2381 2.4344 2.3478 2.6877 2.5432 3.3390 1.7831 1.5984 2.8676
65 3661155 1.2407 2.4171 2.3481 2.6371 2.5442 3.3462 1.7786 1.5905 2.8917
66 3356365 1.2473 2.4580 2.3022 2.6649 2.5919 3.3577 1.7872 1.6349 2.9004
67 3344440 1.2551 2.4651 2.2399 2.7038 2.5110 3.3539 1.8445 1.6544 2.9008
68 3338684 1.2610 2.4588 2.2202 2.6928 2.5273 3.3417 1.9138 1.6508 2.8825
69 3404294 1.2656 2.4345 2.2728 2.7103 2.5734 3.3592 1.9236 1.6402 2.8648
70 3289319 1.2599 2.4610 2.3694 2.7446 2.5698 3.3419 1.8583 1.6354 2.8186
71 3469252 1.2787 2.4573 2.3844 2.7781 2.6008 3.3488 1.7614 1.6739 2.8019
72 3571850 1.2859 2.4721 2.4129 2.7875 2.6202 3.3350 1.7984 1.6679 2.8320
73 3639914 1.2781 2.4808 2.3703 2.7499 2.6286 3.3421 1.8078 1.6975 2.9729
74 3091730 1.2745 2.4331 2.3678 2.6861 2.6275 3.3648 1.7988 1.6422 2.9822
75 3078149 1.3028 2.4226 2.3573 2.6897 2.6157 3.3664 1.8101 1.6592 2.9523
76 3188115 1.2951 2.4477 2.2981 2.6939 2.6520 3.3774 1.8001 1.6638 2.9461
77 3246082 1.2793 2.4289 2.1834 2.7185 2.6326 3.3735 1.7969 1.6544 2.9571
78 3486992 1.2857 2.4430 2.2247 2.6911 2.6407 3.3637 1.7648 1.6601 2.9561
79 3378187 1.2678 2.4194 2.2438 2.7021 2.6535 3.3894 1.7629 1.6685 2.9755
80 3282306 1.2646 2.4325 2.1688 2.6811 2.6626 3.3806 1.7622 1.6664 2.9839
81 3288345 1.2720 2.4281 2.2284 2.7006 2.6419 3.3882 1.7818 1.6510 2.9808
82 3325749 1.2692 2.3966 2.2726 2.7180 2.6455 3.3876 1.7726 1.6346 3.0123
83 3352262 1.2736 2.3909 2.2531 2.7014 2.6774 3.3845 1.8017 1.6741 2.9964
84 3531954 1.2771 2.4206 2.2456 2.6619 2.6770 3.3887 1.8011 1.6621 2.9805
85 3722622 1.2907 2.4086 2.2671 2.6798 2.6540 3.3944 1.8091 1.6791 2.9153
86 3809365 1.2823 2.3697 2.2782 2.6544 2.6518 3.3718 1.7947 1.6592 2.9141
87 3750617 1.2975 2.3821 2.3244 2.6892 2.6660 3.4668 1.8145 1.6596 3.0144
88 3615286 1.2964 2.3712 2.3281 2.6623 2.6677 3.5198 1.8089 1.6624 3.0176
89 3696556 1.3026 2.3817 2.2963 2.6938 2.5743 3.5015 1.7856 1.6588 2.9250
90 4123959 1.2948 2.3711 2.2998 2.6950 2.6609 3.4849 1.7905 1.6646 2.9262
91 4136163 1.2970 2.3975 2.2011 2.6535 2.6602 3.4844 1.7874 1.6601 2.9760
92 3933392 1.2896 2.6581 2.1464 2.6567 2.6288 3.4891 1.7898 1.6737 2.9952
93 4035576 1.2823 2.4968 2.1477 2.6310 2.5838 3.4709 1.7997 1.6529 2.9656
94 4551202 1.3029 2.6003 2.1254 2.6504 2.6408 3.4544 1.8594 1.7204 3.0132
95 4032195 1.2990 2.6425 2.1157 2.5955 2.6396 3.4419 1.9336 1.7326 3.0184
96 3970893 1.3086 2.6739 2.1699 2.5684 2.6780 3.4456 1.8056 1.7151 3.0246
97 4489016 1.3161 2.7162 2.1976 2.6492 2.6473 3.4710 1.6959 1.7377 3.0339
98 5426127 1.3310 2.7279 2.2091 2.6929 2.6357 3.4572 1.7363 1.7592 3.0140
99 4578224 1.3155 2.4848 2.2816 2.7104 2.6568 3.5125 1.7383 1.7369 2.8995
100 4126390 1.3019 2.4149 2.3217 2.7185 2.6242 3.4518 1.7288 1.7133 2.9048
101 4892100 1.3125 2.4700 2.3240 2.7290 2.5959 3.4650 1.7622 1.7467 2.9634
102 4128697 1.3047 2.5361 2.3311 2.7247 2.5745 3.4695 1.7535 1.7140 2.9492
103 4408721 1.3048 2.5626 2.3180 2.7307 2.6402 3.4608 1.7931 1.7197 2.9427
104 4199465 1.2965 2.5174 2.3101 2.7170 2.6114 3.4689 1.7925 1.7207 2.9386
105 4074767 1.2917 2.5181 2.3337 2.7021 2.6044 3.4658 1.8276 1.7093 2.9521
106 4161758 1.2946 2.5059 2.3117 2.7189 2.5808 3.4367 1.8196 1.6986 2.9405
107 3891319 1.3014 2.5068 2.1893 2.7045 2.6334 3.4753 1.8494 1.6655 2.9891
108 4470302 1.2996 2.5076 2.1303 2.7173 2.6266 3.4940 1.8534 1.6939 2.9779
109 4283111 1.2939 2.4635 2.1530 2.6965 2.6261 3.4986 1.8624 1.6935 2.9656
110 3845962 1.2659 2.4482 2.2520 2.6532 2.6219 3.4817 1.8317 1.6880 2.9391
111 3911471 1.2591 2.4496 2.2873 2.6568 2.5764 3.5217 1.8734 1.7178 2.9923
112 3798478 1.2506 2.4275 2.2874 2.6884 2.6527 3.5067 1.8806 1.6938 2.9913
113 3644313 1.2638 2.4233 2.2936 2.7027 2.6711 3.5260 1.9179 1.7262 2.9611
114 3784029 1.2661 2.3870 2.3180 2.7110 2.6323 3.5443 1.9145 1.7150 2.9894
115 3647134 1.2596 2.3949 2.3151 2.6879 2.6333 3.5167 1.9304 1.7398 2.9760
116 3994662 1.2542 2.3853 2.3403 2.7191 2.6491 3.4653 1.9046 1.7543 3.0096
117 3607836 1.2539 2.4133 2.3204 2.7146 2.5823 3.4967 1.9119 1.7148 2.9680
118 3566008 1.2548 2.3681 2.1607 2.7095 2.3672 3.5128 1.8923 1.7246 2.9793
119 3511412 1.2606 2.3832 1.8827 2.7356 2.6429 3.5276 1.9149 1.7249 2.9455
120 3258665 1.2614 2.4082 2.0185 2.8202 2.6340 3.5227 1.9172 1.7247 2.9485
121 3486573 1.2626 2.4641 2.0727 2.7569 2.6312 3.5438 1.9138 1.7178 2.9581
122 3369443 1.2719 2.4734 2.2192 2.7722 2.7039 3.5446 1.9508 1.7363 3.0063
123 3465544 1.2798 2.4978 2.3992 2.7747 2.6937 3.5515 1.9510 1.7367 3.0513
124 3905224 1.2909 2.5224 2.4687 2.8096 2.6996 3.5635 1.9719 1.7840 3.0844
125 3733881 1.3027 2.5336 2.4277 2.7732 2.6797 3.5866 1.9727 1.7663 3.0785
126 3220642 1.2565 2.5136 2.3863 2.7560 2.6850 3.5616 1.9428 1.7169 3.0362
127 3225812 1.2509 2.4282 2.3860 2.7331 2.6891 3.5248 1.9311 1.7318 3.0287
128 3354461 1.2624 2.5319 2.3886 2.7156 2.6870 3.5410 1.9221 1.7135 3.0139
129 3352261 1.2486 2.5218 2.3603 2.7327 2.6654 3.5510 1.9338 1.7101 3.0868
130 3450652 1.2383 2.4867 2.3185 2.7075 2.6610 3.5552 1.9111 1.7224 3.1237
PWABR PSOCOLA PSOBIT PSPORT BUDBEER BUDCHIL BUDAMB BUDWATER BUDSISSS
1 0.4830 0.9489 1.1893 1.7715 8890176 484573.7 2254011 6304844 10064618
2 0.4849 0.9677 1.1862 1.8036 8194413 478105.6 2013875 5471891 11338363
3 0.4860 0.9693 1.2060 1.7653 7722000 506038.6 2308944 5581708 9435079
4 0.4877 0.9467 1.1882 1.7768 7769178 508171.2 2278649 5421028 8143581
5 0.4858 0.9574 1.1892 1.8121 7449343 468388.0 2109718 5136152 7775342
6 0.4910 0.9608 1.1870 1.8162 7929370 466709.4 2070365 4948893 7656876
7 0.4883 0.9394 1.1736 1.8184 7473017 499052.6 2041975 4866528 8203164
8 0.4866 0.9593 1.1817 1.8251 7472424 499696.8 2130112 5110882 8447687
9 0.4884 0.9750 1.1724 1.7774 7292436 456661.5 2012391 4775552 8482877
10 0.4876 0.9736 1.1860 1.7920 7215340 467478.2 1995215 4690143 8131924
11 0.4922 0.9579 1.2090 1.7957 7216230 453125.9 1959695 4521167 8184292
12 0.4883 0.9517 1.1703 1.7744 7378041 449583.6 2079820 4618744 8006102
13 0.4878 0.9633 1.2087 1.8084 7877412 423895.6 2201750 4921010 8052832
14 0.4922 0.9649 1.2071 1.8223 7158125 460454.3 1980527 4739711 7854934
15 0.4958 0.9688 1.2153 1.7983 7137912 454104.7 2023721 4767867 7609626
16 0.4945 0.9648 1.1871 1.8126 7290803 453042.3 2136317 4856393 7640934
17 0.4955 0.9458 1.2188 1.7889 7425266 433081.7 2205673 4684931 8422297
18 0.4983 0.9534 1.2459 1.8015 7450430 460163.3 2163485 4583205 7980377
19 0.5086 0.9374 1.2340 1.8192 9214042 421050.9 2844091 5216686 9541323
20 0.5075 0.9370 1.2241 1.8048 8158864 435182.1 2458147 4583585 8839590
21 0.5062 0.9786 1.2325 1.8148 6515759 495363.4 1972304 4307098 7677033
22 0.4998 0.9312 1.2118 1.7409 6308487 472804.8 2153601 4748004 8354688
23 0.4971 0.9316 1.2162 1.7753 6366367 452920.8 2066530 4710073 8150927
24 0.4993 0.9616 1.2292 1.7880 6770097 450870.4 2152437 4867230 7846633
25 0.4916 0.9670 1.1842 1.7505 6700697 472550.5 2189294 4794611 8461058
26 0.4938 0.9729 1.2116 1.7549 7140792 462772.3 2253024 4883881 8425126
27 0.4961 0.9453 1.2185 1.7171 6891715 507189.1 2151817 4711492 8351766
28 0.4982 0.9538 1.2234 1.7579 7057521 513234.9 2141496 4810043 7956264
29 0.5010 0.9632 1.2250 1.7682 6806593 602342.3 2240864 5020983 8502847
30 0.4994 0.9764 1.2261 1.7634 7068776 638260.4 2198530 5071676 8671279
31 0.4994 0.9783 1.2229 1.7767 6868085 618068.3 2213237 5096684 8230049
32 0.5016 0.9710 1.2031 1.7400 7245015 607338.2 2252202 5263979 8404517
33 0.4921 0.9787 1.2141 1.7786 7160726 1002378.8 2419597 5523848 8872254
34 0.4974 0.9821 1.1945 1.7110 7927365 755301.7 2334515 5259355 9651748
35 0.4984 0.9892 1.2390 1.7631 8275238 724579.8 2155819 5044615 9070647
36 0.4868 0.9870 1.2403 1.6563 7510220 706446.6 2532345 5875038 8649186
37 0.5028 0.9699 1.2282 1.6909 7751398 991277.6 2221561 5321561 9030492
38 0.4989 0.9769 1.2344 1.7361 8701633 852995.5 2302538 5261199 9069668
39 0.5015 0.9686 1.2281 1.7815 8164755 673183.0 2350319 5621057 9116009
40 0.5096 0.9713 1.2368 1.7949 8534307 686730.2 2287028 5303894 10336764
41 0.5069 0.9750 1.2363 1.7912 8333017 768402.5 2262802 5325086 8941018
42 0.5027 0.9821 1.2299 1.7979 8568251 720602.9 2641195 6602036 10163717
43 0.5066 0.9721 1.2181 1.7230 8613013 688646.1 2886395 7354948 10028886
44 0.5123 0.9723 1.2131 1.8039 9139357 717092.7 2430852 6231237 10190148
45 0.5083 0.9803 1.2677 1.8070 8385716 806355.7 2412703 6066821 11198930
46 0.5083 0.9783 1.2275 1.7787 8451237 649994.8 2365468 6209715 10355548
47 0.5109 0.9456 1.2166 1.7704 9033401 540044.0 2057798 5353594 9396952
48 0.5083 0.9664 1.1885 1.7398 8565930 591115.2 2390239 6427650 9238064
49 0.5069 0.9719 1.1829 1.7573 8562307 493197.1 2456918 6941697 9286880
50 0.5086 0.9708 1.2104 1.7510 9255216 574142.3 2048758 5514399 10943146
51 0.5101 0.9690 1.2517 1.7580 10502760 545219.9 2513095 7322716 11297607
52 0.5109 0.9765 1.2428 1.7853 10855161 484422.8 2887292 9651951 9982802
53 0.5077 0.9803 1.2158 1.7696 9473338 561619.5 2295291 6686974 11849225
54 0.5220 0.9776 1.2207 1.7613 8521439 554666.8 2160295 5573380 9895998
55 0.5062 0.9617 1.1762 1.7560 8169912 695658.1 2430452 5428766 10512292
56 0.5158 0.9819 1.1878 1.7765 8705590 694558.9 2381670 5352882 10001971
57 0.5128 1.0029 1.1969 1.6700 8600302 613095.3 2215665 5114736 9450060
58 0.5155 1.0117 1.2031 1.7499 7884570 602932.9 2350453 5800681 9047810
59 0.5105 1.0155 1.1965 1.8116 7509946 614260.3 2263940 5430653 9034858
60 0.5106 1.0054 1.1876 1.8100 7796000 580581.2 2223827 5325139 9626461
61 0.5077 1.0110 1.1711 1.8314 7651158 617712.8 2071658 4874369 8887882
62 0.5086 1.0194 1.2049 1.8433 7430052 605519.3 2118606 4747271 8699165
63 0.5052 1.0131 1.2209 1.8520 7581024 609842.5 1980701 4500918 8756626
64 0.5006 1.0120 1.2089 1.8345 8431470 592139.8 2141976 4660010 9120578
65 0.4961 1.0109 1.2103 1.8250 7903994 582844.1 2262595 4916788 9410935
66 0.5017 1.0229 1.2381 1.8398 7462642 614645.5 2044949 4649568 8540660
67 0.5005 1.0168 1.2195 1.8318 7424743 607572.3 2055490 4677774 8577630
68 0.5069 1.0167 1.2116 1.8221 7480504 620834.6 2111968 4862450 8963865
69 0.5081 1.0055 1.2127 1.8254 7863944 581937.9 2153156 4836102 8831677
70 0.5095 1.0092 1.2137 1.8471 7703698 609332.7 2149987 4707458 8680975
71 0.5323 0.9907 1.2169 1.8627 8508132 619133.2 2805043 5364205 10889743
72 0.5155 0.9962 1.2294 1.8552 8933008 572585.1 2449477 4351596 9842291
73 0.5046 1.0118 1.2364 1.8544 8491850 599515.9 2168905 4208876 8005657
74 0.5019 0.9881 1.2121 1.8444 6940275 655034.4 2218929 5062032 8714475
75 0.5033 1.0049 1.2040 1.8438 6917191 668501.5 2144176 4893322 8555468
76 0.5005 1.0150 1.2180 1.8304 7096722 666124.0 2170967 4848894 8571300
77 0.4996 1.0119 1.2086 1.8324 7105114 732417.3 2240876 4922093 8764326
78 0.5061 1.0088 1.2060 1.8568 7647797 702229.1 2330906 5351141 9089938
79 0.5049 1.0101 1.2006 1.8333 7440408 684271.4 2188360 5017799 8778446
80 0.5037 1.0265 1.2127 1.8348 7255613 633638.0 2067367 4923300 8809264
81 0.5072 1.0214 1.2103 1.8029 7231703 693374.4 2189597 4915221 9521789
82 0.5168 1.0194 1.2157 1.8206 7278022 707615.8 2356724 5348984 9438993
83 0.5188 1.0331 1.2224 1.8076 7382680 722553.2 2250295 5135063 9045288
84 0.5174 1.0282 1.2044 1.8068 7622740 712532.2 2243913 5339400 9272049
85 0.5163 1.0159 1.2253 1.7630 8295038 687023.1 2172504 5122639 9978418
86 0.4970 1.0193 1.2116 1.7315 8136158 646716.0 2301051 5710269 9776284
87 0.4922 1.0226 1.2373 1.7623 8240817 657284.1 2245784 5187058 9601480
88 0.4936 1.0151 1.2217 1.6130 7993962 701042.4 2159896 5277273 11193789
89 0.4879 1.0284 1.2446 1.7259 7997958 744939.0 2374240 5431043 9607554
90 0.4854 1.0194 1.2346 1.7100 8914911 823561.4 2533022 6064885 9870457
91 0.4957 1.0169 1.2533 1.7804 9082346 810516.3 2419167 5849883 10260040
92 0.5104 1.0219 1.2294 1.7537 8690947 755964.4 2379061 5763961 9578120
93 0.5005 1.0309 1.2384 1.7660 8678669 707346.5 2264684 5612253 9693065
94 0.5033 1.0229 1.2127 1.7014 9768461 727180.9 2378165 5996108 12413462
95 0.5006 1.0194 1.1914 1.6946 8751448 1110334.5 2536093 6163859 13143933
96 0.5018 1.0247 1.1991 1.7259 8737854 939273.6 2559486 6806073 11118547
97 0.4893 1.0230 1.2380 1.7388 9684075 842498.6 2340159 5770678 11289800
98 0.4930 1.0218 1.2282 1.7355 11529582 785787.6 2235562 5305632 11573959
99 0.4860 1.0024 1.2275 1.7866 9854882 812169.3 2300728 5714880 10511958
100 0.4917 1.0354 1.2394 1.8376 9030507 730023.4 2090042 5307840 12515693
101 0.4811 1.0394 1.2411 1.8184 10656814 823032.5 1976051 4951640 12966759
102 0.4866 1.0317 1.2149 1.8172 9111428 976730.7 2104956 5576975 10668160
103 0.4915 1.0160 1.2037 1.8294 9642906 738605.6 2489023 6787849 13948692
104 0.4921 1.0406 1.1912 1.8169 9217060 685173.0 2598916 7685812 16087616
105 0.4871 1.0456 1.3122 1.8227 8816389 642518.6 2302455 6451885 12159456
106 0.4832 1.0318 1.2216 1.8303 9074790 677848.7 2427969 5521297 10633146
107 0.4808 0.9944 1.2304 1.8165 8601172 826347.5 2132820 5268035 10770809
108 0.4822 1.0263 1.2393 1.8657 9735782 757562.4 2560376 6159480 10548925
109 0.4823 1.0767 1.2306 1.8873 9222117 825217.4 2454605 6391178 10123204
110 0.4874 1.0925 1.2280 1.9577 8197462 831800.1 2169005 5446149 11471988
111 0.4809 1.0840 1.2478 1.9650 8161117 890943.7 2072759 5055640 10599314
112 0.4790 1.0473 1.2443 1.9478 8085780 818812.4 2201360 5234681 10501150
113 0.4959 1.0635 1.2874 1.9661 7777563 813389.4 2215184 5456357 9476948
114 0.4800 1.0403 1.2843 1.9592 8192525 791213.0 2140796 5055154 9854999
115 0.4827 1.0607 1.2593 1.9530 8222640 753161.9 2064345 4986559 9020688
116 0.4829 1.0644 1.2245 1.9561 8852425 744738.3 2246763 5314687 9639666
117 0.4740 1.0712 1.2311 1.9576 8047626 740853.2 2196948 5029952 10016963
118 0.4772 1.0848 1.2505 1.9549 8079925 828505.4 1987852 4569712 9221363
119 0.4789 1.0773 1.2476 1.9183 8099820 764325.4 2013311 4661941 9163961
120 0.4806 1.0542 1.2487 1.9012 7444464 779151.6 2024477 4649692 9600997
121 0.4816 1.0385 1.2677 1.9330 8060967 780635.1 2175719 4883549 9629093
122 0.4832 1.0442 1.2609 1.9171 7904184 772651.9 2459717 4927239 9266651
123 0.4853 1.0433 1.2563 1.9332 8532755 796750.8 2436148 5077345 11454028
124 0.4955 1.0402 1.2499 1.8988 10077590 774563.7 2533141 4551562 10051577
125 0.4900 1.0589 1.2522 1.9184 9163186 781544.8 2438635 4807379 8887058
126 0.4892 1.0823 1.2450 1.9011 7027349 846743.7 2294455 5560041 9590767
127 0.5220 1.0825 1.2487 1.8955 7000371 852582.7 2233829 5637553 9269821
128 0.5214 1.0805 1.2335 1.8871 7234027 837685.9 2231864 5502635 9242497
129 0.5145 1.0685 1.2267 1.8848 7166769 872753.1 2248620 5354221 9621983
130 0.5383 1.0613 1.2389 1.8759 7538708 863745.7 2348107 5707447 10101244
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PBEPIL PBELUX PBABD PBFRU PBEPAL
6.455e+06 -2.898e+06 1.691e+05 -2.176e+04 -2.754e+05 5.483e+05
PBESTO PBEWIT PBENA PCHSAN PWABR PSOCOLA
1.765e+05 -7.541e+05 -4.195e+05 -1.452e+04 -1.367e+06 2.136e+05
PSOBIT PSPORT BUDBEER BUDCHIL BUDAMB BUDWATER
-5.351e+05 -5.198e+05 4.570e-01 2.898e-01 -5.404e-01 1.600e-01
BUDSISSS
-9.597e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-219170 -46623 3043 36595 230026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.455e+06 9.590e+05 6.731 7.72e-10 ***
PBEPIL -2.898e+06 5.428e+05 -5.340 5.00e-07 ***
PBELUX 1.691e+05 1.579e+05 1.071 0.286502
PBABD -2.176e+04 1.007e+05 -0.216 0.829227
PBFRU -2.754e+05 1.895e+05 -1.454 0.148878
PBEPAL 5.483e+05 1.843e+05 2.976 0.003585 **
PBESTO 1.765e+05 2.359e+05 0.748 0.455853
PBEWIT -7.541e+05 2.052e+05 -3.675 0.000367 ***
PBENA -4.195e+05 1.871e+05 -2.242 0.026945 *
PCHSAN -1.452e+04 1.121e+05 -0.130 0.897169
PWABR -1.367e+06 8.816e+05 -1.550 0.123942
PSOCOLA 2.136e+05 5.344e+05 0.400 0.690108
PSOBIT -5.351e+05 4.358e+05 -1.228 0.222099
PSPORT -5.198e+05 1.877e+05 -2.768 0.006602 **
BUDBEER 4.570e-01 1.497e-02 30.533 < 2e-16 ***
BUDCHIL 2.898e-01 9.954e-02 2.912 0.004346 **
BUDAMB -5.404e-01 6.210e-02 -8.702 3.41e-14 ***
BUDWATER 1.600e-01 1.681e-02 9.522 4.53e-16 ***
BUDSISSS -9.597e-03 9.509e-03 -1.009 0.315070
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 85980 on 111 degrees of freedom
Multiple R-squared: 0.9672, Adjusted R-squared: 0.9619
F-statistic: 181.7 on 18 and 111 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.579380e-01 0.8841240946 0.4420620
[2,] 3.886303e-01 0.7772606191 0.6113697
[3,] 2.956903e-01 0.5913805998 0.7043097
[4,] 1.871071e-01 0.3742142104 0.8128929
[5,] 1.480023e-01 0.2960046478 0.8519977
[6,] 8.738320e-02 0.1747664006 0.9126168
[7,] 6.491372e-02 0.1298274483 0.9350863
[8,] 5.476460e-02 0.1095291943 0.9452354
[9,] 3.866738e-02 0.0773347667 0.9613326
[10,] 2.172227e-02 0.0434445390 0.9782777
[11,] 1.432662e-02 0.0286532492 0.9856734
[12,] 7.608785e-03 0.0152175696 0.9923912
[13,] 6.757347e-03 0.0135146944 0.9932427
[14,] 6.057470e-03 0.0121149393 0.9939425
[15,] 3.377856e-03 0.0067557128 0.9966221
[16,] 1.805711e-03 0.0036114215 0.9981943
[17,] 1.114762e-03 0.0022295247 0.9988852
[18,] 5.959341e-04 0.0011918682 0.9994041
[19,] 4.795301e-03 0.0095906011 0.9952047
[20,] 2.974347e-03 0.0059486931 0.9970257
[21,] 2.464435e-03 0.0049288691 0.9975356
[22,] 1.430952e-03 0.0028619045 0.9985690
[23,] 1.276913e-03 0.0025538267 0.9987231
[24,] 1.366437e-03 0.0027328733 0.9986336
[25,] 9.061809e-04 0.0018123618 0.9990938
[26,] 5.330458e-04 0.0010660916 0.9994670
[27,] 2.834500e-04 0.0005668999 0.9997166
[28,] 3.034776e-04 0.0006069552 0.9996965
[29,] 1.712721e-04 0.0003425441 0.9998287
[30,] 2.219745e-04 0.0004439490 0.9997780
[31,] 1.929866e-04 0.0003859732 0.9998070
[32,] 1.141387e-04 0.0002282775 0.9998859
[33,] 6.001971e-05 0.0001200394 0.9999400
[34,] 1.104346e-03 0.0022086920 0.9988957
[35,] 1.788837e-02 0.0357767308 0.9821116
[36,] 2.692432e-02 0.0538486492 0.9730757
[37,] 2.019936e-02 0.0403987125 0.9798006
[38,] 1.504898e-02 0.0300979615 0.9849510
[39,] 1.036586e-02 0.0207317115 0.9896341
[40,] 7.885683e-03 0.0157713668 0.9921143
[41,] 1.950763e-02 0.0390152647 0.9804924
[42,] 1.339184e-02 0.0267836757 0.9866082
[43,] 9.068019e-03 0.0181360387 0.9909320
[44,] 6.798573e-03 0.0135971451 0.9932014
[45,] 1.537432e-02 0.0307486393 0.9846257
[46,] 1.100816e-02 0.0220163150 0.9889918
[47,] 8.093174e-03 0.0161863483 0.9919068
[48,] 7.451204e-03 0.0149024088 0.9925488
[49,] 1.151102e-02 0.0230220344 0.9884890
[50,] 1.190368e-02 0.0238073680 0.9880963
[51,] 2.477381e-02 0.0495476285 0.9752262
[52,] 2.016622e-02 0.0403324362 0.9798338
[53,] 1.557886e-02 0.0311577241 0.9844211
[54,] 1.187146e-02 0.0237429251 0.9881285
[55,] 8.162468e-03 0.0163249356 0.9918375
[56,] 6.392175e-03 0.0127843507 0.9936078
[57,] 4.664569e-03 0.0093291371 0.9953354
[58,] 3.990025e-03 0.0079800496 0.9960100
[59,] 6.215920e-03 0.0124318397 0.9937841
[60,] 4.263742e-03 0.0085274842 0.9957363
[61,] 2.776196e-03 0.0055523920 0.9972238
[62,] 1.732473e-03 0.0034649465 0.9982675
[63,] 1.074290e-03 0.0021485791 0.9989257
[64,] 8.239749e-04 0.0016479498 0.9991760
[65,] 5.281933e-04 0.0010563866 0.9994718
[66,] 3.534893e-04 0.0007069786 0.9996465
[67,] 7.714363e-04 0.0015428726 0.9992286
[68,] 1.377258e-03 0.0027545161 0.9986227
[69,] 7.991448e-04 0.0015982895 0.9992009
[70,] 5.167851e-04 0.0010335701 0.9994832
[71,] 1.752929e-03 0.0035058574 0.9982471
[72,] 3.841277e-03 0.0076825543 0.9961587
[73,] 2.614468e-03 0.0052289365 0.9973855
[74,] 1.069082e-02 0.0213816471 0.9893092
[75,] 1.223139e-02 0.0244627762 0.9877686
[76,] 2.357807e-02 0.0471561481 0.9764219
[77,] 4.742532e-02 0.0948506371 0.9525747
[78,] 3.293912e-02 0.0658782419 0.9670609
[79,] 3.634775e-02 0.0726955005 0.9636522
[80,] 2.463898e-01 0.4927795244 0.7536102
[81,] 6.013367e-01 0.7973265210 0.3986633
[82,] 5.084312e-01 0.9831376249 0.4915688
[83,] 4.122023e-01 0.8244046614 0.5877977
[84,] 3.239274e-01 0.6478547608 0.6760726
[85,] 2.720202e-01 0.5440403271 0.7279798
[86,] 3.500015e-01 0.7000029668 0.6499985
[87,] 2.872297e-01 0.5744594361 0.7127703
> postscript(file="/var/wessaorg/rcomp/tmp/1ks281333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2tqa31333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3h3nf1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4lmt61333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5eyau1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 130
Frequency = 1
1 2 3 4 5 6
-43726.8984 -16122.2542 65846.1090 97101.0680 29790.9689 123959.0909
7 8 9 10 11 12
41091.1148 -631.8162 26969.8767 -98176.2371 -92054.1135 -174375.6865
13 14 15 16 17 18
-19412.7909 -65406.5346 -62623.9243 -9898.1413 -32507.8739 -115997.9927
19 20 21 22 23 24
-79435.3887 2997.6177 -20031.2150 47401.5799 21380.3787 64942.5287
25 26 27 28 29 30
76446.4192 23395.3768 31730.9475 45460.2030 -814.1659 36874.8147
31 32 33 34 35 36
23052.3408 52858.8577 -36335.5273 -29765.7419 -56075.9035 31757.7835
37 38 39 40 41 42
-55419.8566 -23855.6997 31076.0188 200852.4699 19976.2545 -48918.6777
43 44 45 46 47 48
-17358.2159 -79235.3028 22820.5925 20083.0227 42565.9102 2330.0670
49 50 51 52 53 54
-50473.2473 -20011.7727 42851.4909 -75654.7980 -47588.0554 4954.4965
55 56 57 58 59 60
149727.5735 230025.7859 129686.2562 39787.5729 -13546.7332 -16649.9413
61 62 63 64 65 66
1774.6322 110755.8520 -23809.5090 -12547.3463 38379.6437 -113546.2689
67 68 69 70 71 72
8180.6617 35674.6700 -33403.8752 -109075.7080 4670.3674 -95679.6046
73 74 75 76 77 78
-14778.1727 -42783.1292 35007.2152 25658.8400 50728.6277 41302.4428
79 80 81 82 83 84
-64834.1639 -133069.5060 -24085.6635 21490.8448 -4508.8394 11330.1828
85 86 87 88 89 90
-34749.2809 20400.4231 29999.4051 -155166.7329 106045.3311 4574.0044
91 92 93 94 95 96
-32820.6447 -104673.7221 1818.8473 90614.8617 3194.9329 -200264.1628
97 98 99 100 101 102
-50193.4625 180447.1015 42259.3555 -17778.5106 30677.6984 -173372.4398
103 104 105 106 107 108
-16745.3791 -103420.5234 35449.4735 171318.2600 -54546.6760 149619.0989
109 110 111 112 113 114
66784.7538 35753.9957 154595.6547 75227.2308 148688.0653 137636.5212
115 116 117 118 119 120
-57676.3687 21593.6461 28787.5921 20250.6433 -164756.9806 -79376.3571
121 122 123 124 125 126
-87390.5884 30641.8207 -145571.4363 -219170.2465 -56628.1411 -1793.3587
127 128 129 130
3087.8948 31291.5265 58754.3153 -13939.7172
> postscript(file="/var/wessaorg/rcomp/tmp/6zuww1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 -43726.8984 NA
1 -16122.2542 -43726.8984
2 65846.1090 -16122.2542
3 97101.0680 65846.1090
4 29790.9689 97101.0680
5 123959.0909 29790.9689
6 41091.1148 123959.0909
7 -631.8162 41091.1148
8 26969.8767 -631.8162
9 -98176.2371 26969.8767
10 -92054.1135 -98176.2371
11 -174375.6865 -92054.1135
12 -19412.7909 -174375.6865
13 -65406.5346 -19412.7909
14 -62623.9243 -65406.5346
15 -9898.1413 -62623.9243
16 -32507.8739 -9898.1413
17 -115997.9927 -32507.8739
18 -79435.3887 -115997.9927
19 2997.6177 -79435.3887
20 -20031.2150 2997.6177
21 47401.5799 -20031.2150
22 21380.3787 47401.5799
23 64942.5287 21380.3787
24 76446.4192 64942.5287
25 23395.3768 76446.4192
26 31730.9475 23395.3768
27 45460.2030 31730.9475
28 -814.1659 45460.2030
29 36874.8147 -814.1659
30 23052.3408 36874.8147
31 52858.8577 23052.3408
32 -36335.5273 52858.8577
33 -29765.7419 -36335.5273
34 -56075.9035 -29765.7419
35 31757.7835 -56075.9035
36 -55419.8566 31757.7835
37 -23855.6997 -55419.8566
38 31076.0188 -23855.6997
39 200852.4699 31076.0188
40 19976.2545 200852.4699
41 -48918.6777 19976.2545
42 -17358.2159 -48918.6777
43 -79235.3028 -17358.2159
44 22820.5925 -79235.3028
45 20083.0227 22820.5925
46 42565.9102 20083.0227
47 2330.0670 42565.9102
48 -50473.2473 2330.0670
49 -20011.7727 -50473.2473
50 42851.4909 -20011.7727
51 -75654.7980 42851.4909
52 -47588.0554 -75654.7980
53 4954.4965 -47588.0554
54 149727.5735 4954.4965
55 230025.7859 149727.5735
56 129686.2562 230025.7859
57 39787.5729 129686.2562
58 -13546.7332 39787.5729
59 -16649.9413 -13546.7332
60 1774.6322 -16649.9413
61 110755.8520 1774.6322
62 -23809.5090 110755.8520
63 -12547.3463 -23809.5090
64 38379.6437 -12547.3463
65 -113546.2689 38379.6437
66 8180.6617 -113546.2689
67 35674.6700 8180.6617
68 -33403.8752 35674.6700
69 -109075.7080 -33403.8752
70 4670.3674 -109075.7080
71 -95679.6046 4670.3674
72 -14778.1727 -95679.6046
73 -42783.1292 -14778.1727
74 35007.2152 -42783.1292
75 25658.8400 35007.2152
76 50728.6277 25658.8400
77 41302.4428 50728.6277
78 -64834.1639 41302.4428
79 -133069.5060 -64834.1639
80 -24085.6635 -133069.5060
81 21490.8448 -24085.6635
82 -4508.8394 21490.8448
83 11330.1828 -4508.8394
84 -34749.2809 11330.1828
85 20400.4231 -34749.2809
86 29999.4051 20400.4231
87 -155166.7329 29999.4051
88 106045.3311 -155166.7329
89 4574.0044 106045.3311
90 -32820.6447 4574.0044
91 -104673.7221 -32820.6447
92 1818.8473 -104673.7221
93 90614.8617 1818.8473
94 3194.9329 90614.8617
95 -200264.1628 3194.9329
96 -50193.4625 -200264.1628
97 180447.1015 -50193.4625
98 42259.3555 180447.1015
99 -17778.5106 42259.3555
100 30677.6984 -17778.5106
101 -173372.4398 30677.6984
102 -16745.3791 -173372.4398
103 -103420.5234 -16745.3791
104 35449.4735 -103420.5234
105 171318.2600 35449.4735
106 -54546.6760 171318.2600
107 149619.0989 -54546.6760
108 66784.7538 149619.0989
109 35753.9957 66784.7538
110 154595.6547 35753.9957
111 75227.2308 154595.6547
112 148688.0653 75227.2308
113 137636.5212 148688.0653
114 -57676.3687 137636.5212
115 21593.6461 -57676.3687
116 28787.5921 21593.6461
117 20250.6433 28787.5921
118 -164756.9806 20250.6433
119 -79376.3571 -164756.9806
120 -87390.5884 -79376.3571
121 30641.8207 -87390.5884
122 -145571.4363 30641.8207
123 -219170.2465 -145571.4363
124 -56628.1411 -219170.2465
125 -1793.3587 -56628.1411
126 3087.8948 -1793.3587
127 31291.5265 3087.8948
128 58754.3153 31291.5265
129 -13939.7172 58754.3153
130 NA -13939.7172
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -16122.2542 -43726.8984
[2,] 65846.1090 -16122.2542
[3,] 97101.0680 65846.1090
[4,] 29790.9689 97101.0680
[5,] 123959.0909 29790.9689
[6,] 41091.1148 123959.0909
[7,] -631.8162 41091.1148
[8,] 26969.8767 -631.8162
[9,] -98176.2371 26969.8767
[10,] -92054.1135 -98176.2371
[11,] -174375.6865 -92054.1135
[12,] -19412.7909 -174375.6865
[13,] -65406.5346 -19412.7909
[14,] -62623.9243 -65406.5346
[15,] -9898.1413 -62623.9243
[16,] -32507.8739 -9898.1413
[17,] -115997.9927 -32507.8739
[18,] -79435.3887 -115997.9927
[19,] 2997.6177 -79435.3887
[20,] -20031.2150 2997.6177
[21,] 47401.5799 -20031.2150
[22,] 21380.3787 47401.5799
[23,] 64942.5287 21380.3787
[24,] 76446.4192 64942.5287
[25,] 23395.3768 76446.4192
[26,] 31730.9475 23395.3768
[27,] 45460.2030 31730.9475
[28,] -814.1659 45460.2030
[29,] 36874.8147 -814.1659
[30,] 23052.3408 36874.8147
[31,] 52858.8577 23052.3408
[32,] -36335.5273 52858.8577
[33,] -29765.7419 -36335.5273
[34,] -56075.9035 -29765.7419
[35,] 31757.7835 -56075.9035
[36,] -55419.8566 31757.7835
[37,] -23855.6997 -55419.8566
[38,] 31076.0188 -23855.6997
[39,] 200852.4699 31076.0188
[40,] 19976.2545 200852.4699
[41,] -48918.6777 19976.2545
[42,] -17358.2159 -48918.6777
[43,] -79235.3028 -17358.2159
[44,] 22820.5925 -79235.3028
[45,] 20083.0227 22820.5925
[46,] 42565.9102 20083.0227
[47,] 2330.0670 42565.9102
[48,] -50473.2473 2330.0670
[49,] -20011.7727 -50473.2473
[50,] 42851.4909 -20011.7727
[51,] -75654.7980 42851.4909
[52,] -47588.0554 -75654.7980
[53,] 4954.4965 -47588.0554
[54,] 149727.5735 4954.4965
[55,] 230025.7859 149727.5735
[56,] 129686.2562 230025.7859
[57,] 39787.5729 129686.2562
[58,] -13546.7332 39787.5729
[59,] -16649.9413 -13546.7332
[60,] 1774.6322 -16649.9413
[61,] 110755.8520 1774.6322
[62,] -23809.5090 110755.8520
[63,] -12547.3463 -23809.5090
[64,] 38379.6437 -12547.3463
[65,] -113546.2689 38379.6437
[66,] 8180.6617 -113546.2689
[67,] 35674.6700 8180.6617
[68,] -33403.8752 35674.6700
[69,] -109075.7080 -33403.8752
[70,] 4670.3674 -109075.7080
[71,] -95679.6046 4670.3674
[72,] -14778.1727 -95679.6046
[73,] -42783.1292 -14778.1727
[74,] 35007.2152 -42783.1292
[75,] 25658.8400 35007.2152
[76,] 50728.6277 25658.8400
[77,] 41302.4428 50728.6277
[78,] -64834.1639 41302.4428
[79,] -133069.5060 -64834.1639
[80,] -24085.6635 -133069.5060
[81,] 21490.8448 -24085.6635
[82,] -4508.8394 21490.8448
[83,] 11330.1828 -4508.8394
[84,] -34749.2809 11330.1828
[85,] 20400.4231 -34749.2809
[86,] 29999.4051 20400.4231
[87,] -155166.7329 29999.4051
[88,] 106045.3311 -155166.7329
[89,] 4574.0044 106045.3311
[90,] -32820.6447 4574.0044
[91,] -104673.7221 -32820.6447
[92,] 1818.8473 -104673.7221
[93,] 90614.8617 1818.8473
[94,] 3194.9329 90614.8617
[95,] -200264.1628 3194.9329
[96,] -50193.4625 -200264.1628
[97,] 180447.1015 -50193.4625
[98,] 42259.3555 180447.1015
[99,] -17778.5106 42259.3555
[100,] 30677.6984 -17778.5106
[101,] -173372.4398 30677.6984
[102,] -16745.3791 -173372.4398
[103,] -103420.5234 -16745.3791
[104,] 35449.4735 -103420.5234
[105,] 171318.2600 35449.4735
[106,] -54546.6760 171318.2600
[107,] 149619.0989 -54546.6760
[108,] 66784.7538 149619.0989
[109,] 35753.9957 66784.7538
[110,] 154595.6547 35753.9957
[111,] 75227.2308 154595.6547
[112,] 148688.0653 75227.2308
[113,] 137636.5212 148688.0653
[114,] -57676.3687 137636.5212
[115,] 21593.6461 -57676.3687
[116,] 28787.5921 21593.6461
[117,] 20250.6433 28787.5921
[118,] -164756.9806 20250.6433
[119,] -79376.3571 -164756.9806
[120,] -87390.5884 -79376.3571
[121,] 30641.8207 -87390.5884
[122,] -145571.4363 30641.8207
[123,] -219170.2465 -145571.4363
[124,] -56628.1411 -219170.2465
[125,] -1793.3587 -56628.1411
[126,] 3087.8948 -1793.3587
[127,] 31291.5265 3087.8948
[128,] 58754.3153 31291.5265
[129,] -13939.7172 58754.3153
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -16122.2542 -43726.8984
2 65846.1090 -16122.2542
3 97101.0680 65846.1090
4 29790.9689 97101.0680
5 123959.0909 29790.9689
6 41091.1148 123959.0909
7 -631.8162 41091.1148
8 26969.8767 -631.8162
9 -98176.2371 26969.8767
10 -92054.1135 -98176.2371
11 -174375.6865 -92054.1135
12 -19412.7909 -174375.6865
13 -65406.5346 -19412.7909
14 -62623.9243 -65406.5346
15 -9898.1413 -62623.9243
16 -32507.8739 -9898.1413
17 -115997.9927 -32507.8739
18 -79435.3887 -115997.9927
19 2997.6177 -79435.3887
20 -20031.2150 2997.6177
21 47401.5799 -20031.2150
22 21380.3787 47401.5799
23 64942.5287 21380.3787
24 76446.4192 64942.5287
25 23395.3768 76446.4192
26 31730.9475 23395.3768
27 45460.2030 31730.9475
28 -814.1659 45460.2030
29 36874.8147 -814.1659
30 23052.3408 36874.8147
31 52858.8577 23052.3408
32 -36335.5273 52858.8577
33 -29765.7419 -36335.5273
34 -56075.9035 -29765.7419
35 31757.7835 -56075.9035
36 -55419.8566 31757.7835
37 -23855.6997 -55419.8566
38 31076.0188 -23855.6997
39 200852.4699 31076.0188
40 19976.2545 200852.4699
41 -48918.6777 19976.2545
42 -17358.2159 -48918.6777
43 -79235.3028 -17358.2159
44 22820.5925 -79235.3028
45 20083.0227 22820.5925
46 42565.9102 20083.0227
47 2330.0670 42565.9102
48 -50473.2473 2330.0670
49 -20011.7727 -50473.2473
50 42851.4909 -20011.7727
51 -75654.7980 42851.4909
52 -47588.0554 -75654.7980
53 4954.4965 -47588.0554
54 149727.5735 4954.4965
55 230025.7859 149727.5735
56 129686.2562 230025.7859
57 39787.5729 129686.2562
58 -13546.7332 39787.5729
59 -16649.9413 -13546.7332
60 1774.6322 -16649.9413
61 110755.8520 1774.6322
62 -23809.5090 110755.8520
63 -12547.3463 -23809.5090
64 38379.6437 -12547.3463
65 -113546.2689 38379.6437
66 8180.6617 -113546.2689
67 35674.6700 8180.6617
68 -33403.8752 35674.6700
69 -109075.7080 -33403.8752
70 4670.3674 -109075.7080
71 -95679.6046 4670.3674
72 -14778.1727 -95679.6046
73 -42783.1292 -14778.1727
74 35007.2152 -42783.1292
75 25658.8400 35007.2152
76 50728.6277 25658.8400
77 41302.4428 50728.6277
78 -64834.1639 41302.4428
79 -133069.5060 -64834.1639
80 -24085.6635 -133069.5060
81 21490.8448 -24085.6635
82 -4508.8394 21490.8448
83 11330.1828 -4508.8394
84 -34749.2809 11330.1828
85 20400.4231 -34749.2809
86 29999.4051 20400.4231
87 -155166.7329 29999.4051
88 106045.3311 -155166.7329
89 4574.0044 106045.3311
90 -32820.6447 4574.0044
91 -104673.7221 -32820.6447
92 1818.8473 -104673.7221
93 90614.8617 1818.8473
94 3194.9329 90614.8617
95 -200264.1628 3194.9329
96 -50193.4625 -200264.1628
97 180447.1015 -50193.4625
98 42259.3555 180447.1015
99 -17778.5106 42259.3555
100 30677.6984 -17778.5106
101 -173372.4398 30677.6984
102 -16745.3791 -173372.4398
103 -103420.5234 -16745.3791
104 35449.4735 -103420.5234
105 171318.2600 35449.4735
106 -54546.6760 171318.2600
107 149619.0989 -54546.6760
108 66784.7538 149619.0989
109 35753.9957 66784.7538
110 154595.6547 35753.9957
111 75227.2308 154595.6547
112 148688.0653 75227.2308
113 137636.5212 148688.0653
114 -57676.3687 137636.5212
115 21593.6461 -57676.3687
116 28787.5921 21593.6461
117 20250.6433 28787.5921
118 -164756.9806 20250.6433
119 -79376.3571 -164756.9806
120 -87390.5884 -79376.3571
121 30641.8207 -87390.5884
122 -145571.4363 30641.8207
123 -219170.2465 -145571.4363
124 -56628.1411 -219170.2465
125 -1793.3587 -56628.1411
126 3087.8948 -1793.3587
127 31291.5265 3087.8948
128 58754.3153 31291.5265
129 -13939.7172 58754.3153
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7zrsj1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89iku1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9bn241333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ec7v1333540836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11gva11333540836.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12i48x1333540836.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/132jr21333540836.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14dnyd1333540836.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15eqxn1333540836.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/169hik1333540836.tab")
+ }
>
> try(system("convert tmp/1ks281333540836.ps tmp/1ks281333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tqa31333540836.ps tmp/2tqa31333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h3nf1333540836.ps tmp/3h3nf1333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lmt61333540836.ps tmp/4lmt61333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/5eyau1333540836.ps tmp/5eyau1333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zuww1333540836.ps tmp/6zuww1333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zrsj1333540836.ps tmp/7zrsj1333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/89iku1333540836.ps tmp/89iku1333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bn241333540836.ps tmp/9bn241333540836.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ec7v1333540836.ps tmp/10ec7v1333540836.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.343 0.676 7.028